1435 research outputs found
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An Exploration of Learning Processes as Process Maps in FLOSS Repositories
No full textEvidence suggests that Free/Libre Open Source Software (FLOSS) environ-ments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while mak-ing use of the tools available in these environments. A number of studies docu-ment the existence of learning processes in FLOSS through the analysis of sur-veys and questionnaires filled by FLOSS project participants. At the same time, the interest in understanding the dynamics of the FLOSS phenomenon, its popu-larity and success resulted in the development of tools and techniques for extract-ing and analyzing data from different FLOSS data sources. This new field is called Mining Software Repositories (MSR). In spite of these efforts, there is limited work aiming to provide empirical evidence of learning processes directly from FLOSS repositories.
In this paper, we seek to trigger such an initiative by proposing an approach based on Process Mining to trace learning behaviors from FLOSS participants’ trails of activities, as recorded in FLOSS repositories, and visualize them as pro-cess maps. Process maps provide a pictorial representation of real behavior as it is recorded in FLOSS data. Our aim is to provide critical evidence that boosts the understanding of learning behavior in FLOSS communities by analyzing the rel-evant repositories. In order to accomplish this, we propose an effective approach that comprises first the mining of FLOSS repositories in order to generate Event logs, and then the generation of process maps, equipped with relevant statistical data interpreting and indicating the value of process discovery from these reposi-tories
Inexact stabilized Benders' decomposition approaches to chance-constrained problems with finite support
No full textMotivated by a class of chance-constrained optimization problems, we explore modifications of the (generalized) Benders' decomposition approach. The chance-constrained problems we consider involve a random variable with an underlying discrete distribution, are convex in the decision variable, but their probabilistic constraint is neither separable nor linear. The variants of Benders' approach we propose exploit advances in cutting-plane procedures developed for the convex case. Specifically, the approach is stabilized in the two ways; via a proximal term/trust region in the L1 norm, or via a level constraint. Furthermore, the approaches can use inexact oracles, in particular informative on-demand inexact ones. The simultaneous use of the two features requires a nontrivial convergence analysis; we provide it under what would seem to be the weakest possible assumptions on the handling of the two parameters controlling the oracle (target and accuracy), strengthening earlier know results. Numerical performance of the approaches are assessed on a class of hybrid robust and chance-constrained conic problems. The numerical results show that the approach has potential, especially for instances that are difficult to solve with standard techniques
Unsupervised Antonym-Synonym Discrimination in Vector Space
No full textSUMMARY.
Automatic detection of antonymy is
an important task in Natural Language
Processing (NLP). However, currently, there is
no effective measure to discriminate antonyms
from synonyms because they share many
common features. In this paper, we introduce
APAnt
, a new Average-Precision-based
measure for the unsupervised identification of
antonymy using Distributional Semantic
Models (DSMs).
APAnt
makes use of Average
Precision to estimate the extent and salience of
the intersection among the most descriptive
contexts of two target words. Evaluation shows
that the proposed method is able to distinguish
antonyms and synonyms with high accuracy,
outperforming a baseline model implementing
the
co-occurrence hypothesis
.
RIASSUNTO.
Sebbene l'identificazione automatica
di antonimi sia un compito fondamentale del
Natural Language Processing (NLP), ad oggi
non esistono sistemi soddisfacenti per risolvere
questo problema. Gli antonimi, infatti,
condividono molte caratteristiche con i
sinonimi, e vengono spesso confusi con essi. In
questo articolo introduciamo APAnt, una
misura basata sull'Average Precision (AP) per
l'identificazione automatica degli antonimi nei
Modelli Distribuzionali (DSMs). APAnt fa uso
dell'AP per stimare il grado e la rilevanza
dell'intersezione tra i contesti più descrittivi di
due parole target. I risultati dimostrano che
APAnt è in grado di distinguere gli antonimi
dai sinonimi con elevata
precisione, superando
la baseline basata sull'ipotesi della co-
occorrenz
A simple and generic interface for a Cloud Monitoring System
No full textThe paper addresses the definition of an ontology for cloud monitoring activities, with the aim of defining a
standard interface for their configuration. To be widely adopted, such ontology must be extremely flexible,
coping with a wide range of use cases: from the minimalist plug-and-play user, to the one governing a complex
infrastructure.
Our work is based on the Open Cloud Computing Interface, that is an open, community driven OGF standard
allowing boundary-level interfaces to be built using RESTful patterns over HTTP. Among others, OpenStack
and OpenNebula adopt OCCI.
Using the OCCI ontology we define two kinds that are associated with the basic components of a monitoring
infrastructure: the collector link, that performs measurements, and the sensor resource, that aggregates data
and undertakes actions.
This paper is a compact and self-contained revision of a document currently under discussion inside the OCCI
community
The Evalita 2014 Dependency Parsing task
No full textSUMMARY.
The Parsing Task is among the “historical” tasks of Evalita, and in all editions its main objective has been to define and improve state-of-the-art technologies for parsing Italian. The 2014’s edition of the shared task features several novelties that have mainly to do with the data set and the subtasks. The paper therefore focuses on these two strictly interrelated aspects and presents an overview of the participants systems and results.
RIASSUNTO.
Il “Parsing Task”, tra i compiti storici di Evalita, in tutte le edizioni ha avuto lo scopo principale di definire ed estendere lo stato dell’arte per l’analisi sin-
tattica automatica della lingua italiana. Nell’edizione del 2014 della campagna di valutazione esso si caratterizza per alcune significative novità legate in particolare ai
dati utilizzati per l’addestramento e alla sua organizzazione interna. L’articolo si focalizza pertanto su questi due aspetti strettamente interrelati e presenta una panoramica dei sistemi che hanno partecipato e dei risultati raggiunti
A Preliminary Application of Echo State Networks to Emotion Recognition
No full textSUMMARY.
This report investigates a pre-
liminary application of Echo State Net-
works (ESNs) to the problem of auto-
matic emotion recognition from speech.
In the proposed approach, speech wave-
form signals are directly used as input
time series for the ESN models, trained
on a multi-classification task over a dis-
crete set of emotions. Within the scopes
of the Emotion Recognition Task of the
Evalita 2014 competition, the performance
of the proposed model is assessed by
considering two emotional Italian speech
corpora, namely the E-Carini corpus and
the emotion corpus. Promising results
show that the proposed system is able to
achieve a very good performance in rec-
ognizing emotions from speech uttered by
a speaker on which it has already been
trained, whereas generalization of the pre-
dictions to speech uttered by unseen sub-
jects is still challenging.
RIASSUNTO.
Questo documento esamina l’applicazione preliminare delle Echo Stato Networks (ESN) per il problema del riconoscimento automatico delle emozioni dal parlato. Nell’approccio proposto, i segnali che rappresentano la forma d’onda del parlato sono usati direttamente come serie temporali di ingresso per i modelli ESN, addestrati su un compito di multiclassificazione su un insieme discreto di emozioni. Entro gli ambiti della Emotion Recognition Task della competizione Evalita 2014, la performance del modello proposto viene valutata considerando due corpora di dati emotivi in lingua Italiana, ovvero il corpus E-Carini e il corpus emotion. I risultati ottenuti sono promettenti e mostrano cheil sistema proposto è in grado di raggiungere una buona prestazione nel riconoscimento di emozioni a partire dalle parole pronunciate da un utente sul quale il sistema è stato già addestrato, mentre la generalizzazione delle predizioni per le frasi pronunciate da soggetti mai visti in fase di addestramento rappresenta ancora un aspetto ambizioso
Cardiovascular effects of arsenic: clinical and epidemiological findings
No full textSeveral population studies relate exposure to high levels of arsenic with an increased incidence of ischemic heart disease and cardiovascular mortality. An association has been shown between exposure to high levels of arsenic and cardiovascular risk factors such as hypertension and diabetes mellitus, and vascular damage such as subclinical carotid atherosclerosis. The mechanisms underlying these phenomena are currently being studied and appear to indicate an alteration of vascular function. However, the effects of low levels of exposure to arsenic and their potential detrimental cardiovascular effect are less explored. The article provides an overview of the pathophysiologic mechanisms linking low-level arsenic exposure to the occurrence of cardiovascular disease and its complications, and some potential preventive strategies to implement
Non-Abelian Vortices with an Aharonov-Bohm Effect
No full textThe interplay of gauge dynamics and flavor symmetries often leads to
remarkably subtle phenomena in the presence of soliton configurations.
Non-Abelian vortices -- vortex solutions with continuous internal orientational
moduli -- provide an example. Here we study the effect of weakly gauging a
U(1)_R subgroup of the flavor symmetry on such BPS vortex solutions. Our
prototypical setting consists of an SU(2) x U(1) gauge theory with N_f=2 sets
of fundamental scalars that break the gauge symmetry to an "electromagnetic"
U(1). The weak U(1)_R gauging converts the well-known CP1 orientation modulus
|B| of the non-Abelian vortex into a parameter characterizing the strength of
the magnetic field that is responsible for the Aharonov-Bohm effect. As the
phase of B remains a genuine zero mode while the electromagnetic gauge symmetry
is Higgsed in the interior of the vortex, these solutions are superconducting
strings
Large-scale Unit Commitment under uncertainty: a literature survey
No full textThe Unit Commitment problem in energy management aims at finding the optimal
productions schedule of a set of generation units while meeting various system-wide
constraints. It has always been a large-scale, non-convex difficult problem, especially
in view of the fact that operational requirements imply that it has to be solved in
an “unreasonably” small time; recently, the ever increasing capacity for renewable
generation has strongly increased the level of uncertainty in the system, making the
(ideal) Unit Commitment model a large-scale, non-convex, (stochastic, ro-bust, chance-constrained) program. We provide a survey of the literature on methods
for the Uncertain Unit Commitment problem, in all its variants. We start with a
review of the main contributions on solution methods for the deterministic versions
of the problem, focussing on those based on mathematical programming techniques
that are more relevant for the uncertain versions of the problem. We then present
and categorize the approaches to the latter, also providing entry points to the relevant
literature on optimization under uncertainty